Details
| Original language | English |
|---|---|
| Article number | 109896 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 186 |
| Early online date | 3 Nov 2022 |
| Publication status | Published - 1 Mar 2023 |
Abstract
Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
Keywords
- Digital twin, Gearbox, Health management, Surface degradation, Wear assessment
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Mechanical Systems and Signal Processing, Vol. 186, 109896, 01.03.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Digital twin-driven intelligent assessment of gear surface degradation
AU - Feng, Ke
AU - Ji, J. C.
AU - Zhang, Yongchao
AU - Ni, Qing
AU - Liu, Zheng
AU - Beer, Michael
N1 - Funding Information: This work was supported by the National Research Council of Canada through the Canada - Germany 3 + 2 Joint Project “Digital Twin Platform for Infrastructure Asset Lifecycle Management” (Agreement No. INT-016-1). The authors are grateful for the support. In addition, the authors would like to thank the assistance provided by the Tribology and Machine Condition Monitoring Lab from the University of New South Wales.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
AB - Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
KW - Digital twin
KW - Gearbox
KW - Health management
KW - Surface degradation
KW - Wear assessment
UR - http://www.scopus.com/inward/record.url?scp=85141308079&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2022.109896
DO - 10.1016/j.ymssp.2022.109896
M3 - Article
AN - SCOPUS:85141308079
VL - 186
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 109896
ER -